Researchers have successfully given AI a curiosity implant, which motivated it to explore a virtual environment.

This could be the bridge between AI and real world application

Researchers at the University of California (UC), Berkeley, have produced an artificial intelligence (AI) that is naturally curious.

While the AI that was not equipped with the curiosity ‘upgrade’ banged into walls repeatedly, the curious AI explored its environment in order to learn more.

This is a useful and effective strategy for teaching AI to complete specific tasks — as shown by the AI who beat the AlphaGo world number one — but less useful when you want a machine to be autonomous and operate outside of direct commands.

This is crucial step to integrating AI into the real world and having it solve real world problems because, as Agrawal says, “rewards in the real world are very sparse.”

Casting algorithm design as a learning problem allows us to specify the class of problems we are interested in through example problem instances.

Each function in the system model could be learned or just implemented directly with some algorithm.

What if instead of hand designing an optimising algorithm (function) we learn it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!